A general constrained minimum risk inequality is derived. Given two densities fθ and f₀ we find a lower bound for the risk at the point θ given an upper bound for the risk at the point 0. The inequality sheds new light on superefficient estimators in the normal location problem and also on an adaptive estimation problem arising in nonparametric functional estimation.
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Brown et al. (1996) studied this question.
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